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1,047 results for “constraint”
Data from: Seasonal time constraints reduce genetic variation in life history traits along a latitudinal gradient
1. Time constraints cause strong selection on life history traits, because populations need to complete their life cycles within a shorter time. We therefore expect lower genetic variation in these traits in high- than in low-latitude populations, since the former are more time constrained. 2. The aim was to estimate life history traits and their genetic variation in an obligately univoltine damselfly along a latitudinal gradient of 2,730 km. 3. Populations were grown in the laboratory at temperatures and photoperiods simulating those at their place of origin. In a complementary experiment individuals from the same families were grown in constant temperature and photoperiod that mimicked average conditions across the latitude. 4. Development time and size was faster and smaller, respectively, and growth rate was higher at northern latitudes. Additive genetic variance was very low for life history traits and estimates for egg development time and larval growth rate showed significant decreases towards northern latitudes. The expression of genetic effects in life history traits differed considerably when individuals were grown in constant rather than simulated and naturally variable conditions. 5. Our results support strong selection by time constraints. They also highlight the importance of growing organisms in their native environment for correct estimates of genetic variance at their place of origin. Our results also suggest that the evolutionary potential of life history traits is very low at northern compared to southern latitudes, but that changes in climate could alter this pattern.
Data from: Comparative analysis of encephalization in mammals reveals relaxed constraints on anthropoid primate and cetacean brain scaling
There is a well-established allometric relationship between brain and body mass in mammals. Deviation of relatively increased brain size from this pattern appears to coincide with enhanced cognitive abilities. To examine whether there is a phylogenetic structure to such episodes of changes in encephalization across mammals, we used phylogenetic techniques to analyse brain mass, body mass and encephalization quotient (EQ) among 630 extant mammalian species. Among all mammals, anthropoid primates and odontocete cetaceans have significantly greater variance in EQ, suggesting that evolutionary constraints that result in a strict correlation between brain and body mass have independently become relaxed. Moreover, ancestral state reconstructions of absolute brain mass, body mass and EQ revealed patterns of increase and decrease in EQ within anthropoid primates and cetaceans. We propose both neutral drift and selective factors may have played a role in the evolution of brain–body allometry.
Data from: Ecological constraints coupled with deep-time habitat dynamics predict the latitudinal diversity gradient in reef fishes
We develop a spatially explicit model of diversification based on paleohabitat to explore the predictions of four major hypotheses potentially explaining the latitudinal diversity gradient (LDG), namely, the 'time-area', 'tropical niche conservatism', 'ecological limits' and 'evolutionary speed' hypotheses. We compare simulation outputs to observed diversity gradients in the global reef fish fauna. Our simulations show that these hypotheses are non-mutually exclusive and that their relative influence depends on the time scale considered. Indeed, simulations suggest that reef habitat dynamics produced the LDG during deep geological time, while ecological constraints shaped the modern LDG, with a strong influence of the reduction in the latitudinal extent of tropical reefs during the Neogene. Overall, this study illustrates how mechanistic models in ecology and evolution can provide a temporal and spatial understanding of the role of speciation, extinction and dispersal in generating contemporary biodiversity patterns.
Data from: Multivariate selection and intersexual genetic constraints in a wild bird population
When traits are genetically correlated between the sexes, the response to selection in one sex can be altered by indirect selection in the other sex, a type of genetic constraint commonly referred to as intralocus sexual conflict (ISC). While potentially common, ISC has rarely been studied in wild populations. In this study, we applied a multivariate framework to quantify the microevolutionary impacts of ISC over a set of morphological traits (wing length, tarsus length, bill depth, and bill length) in a wild population of great tits (Parus major) from Wytham Woods, UK. Specifically, we quantified the impact of cross-sex genetic covariances (the B matrix) on the additive genetic variance for relative fitness expected to be generated by directional selection and additive genetic (co)variance. Together, multivariate sex-specific selection and additive genetic (co)variance were expected to generate additive genetic variance for relative fitness that was uncorrelated between the sexes (cross-sex genetic correlation = -0.003, 95% CI = -0.83, 0.83). Gender load, defined as the expected reduction in additive genetic variance for relative fitness generated by the traits under study due to sex-specific effects, was estimated at 50% (95% CI = 13%, 86%). This study provides novel insights into the evolution of sexual dimorphism in great tits and illustrates how quantitative genetics and selection analyses can be combined in a multivariate framework to quantify the expected microevolutionary impacts of ISC.
Data from: Selection and constraints on offspring size-number trade-offs in sand lizards (Lacerta agilis)
The trade-off between offspring size and number is a central component of life-history theory, postulating that larger investment into offspring size inevitably decreases offspring number. This trade-off is generally discussed in terms of genetic, physiological or morphological constraints; however, as among-individual differences can mask individual trade-offs, the underlying mechanisms may be difficult to reveal. In this study, we use multivariate analyses to investigate whether there is a trade-off between offspring size and number in a population of sand lizards by separating among- and within-individual patterns using a 15-year data set collected in the wild. We also explore the ecological and evolutionary causes and consequences of this trade-off by investigating how a female's resource (condition)- vs. age-related size (snout-vent length) influences her investment into offspring size vs. number (OSN), whether these traits are heritable and under selection and whether the OSN trade-off has a genetic component. We found a negative correlation between offspring size and number within individual females and physical constraints (size of body cavity) appear to limit the number of eggs that a female can produce. This suggests that the OSN trade-off occurs due to resource constraints as a female continues to grow throughout life and, thus, produces larger clutches. In contrast to the assumptions of classic OSN theory, we did not detect selection on offspring size; however, there was directional selection for larger clutch sizes. The repeatabilities of both offspring size and number were low and we did not detect any additive genetic variance in either trait. This could be due to strong selection (past or current) on these life-history traits, or to insufficient statistical power to detect significant additive genetic effects. Overall, the findings of this study are an important illustration of how analyses of within-individual patterns can reveal trade-offs and their underlying causes, with potential evolutionary and ecological consequences that are otherwise hidden by among-individual variation.
Data from: Chewing on the trees: constraints and adaptation in the evolution of the primate mandible
Chewing on different food types is a demanding biological function. The classic assumption in studying the shape of feeding apparatuses is that animals are what they eat, meaning that adaptation to different food items accounts for most of their interspecific variation. Yet, a growing body of evidence points against this concept. We use the primate mandible as a model structure to investigate the complex interplay between shape, size, diet and phylogeny. We find a weak but significant impact of diet on mandible shape variation in primates as a whole but not in anthropoids and catarrhines as tested in isolation. These clades mainly exhibit allometric shape changes which are unrelated to diet. Diet is an important factor in the diversification of strepsirrhines and platyrrhines and a phylogenetic signal is detected in all primate clades. Peaks in morphological disparity occur during the Oligocene (between 37 and 25 Ma) supporting the notion that an adaptive radiation characterized the evolution of South American monkeys. In all primate clades, the evolution of mandible size is faster than its shape pointing to a strong effect of allometry on ecomorphological diversification in this group.
Data from: Age-dependent modulation of songbird summer feather molt by temporal and functional constraints
Time constraints influence various ecological, life-history, and demographic properties of individuals and populations of many species throughout the annual cycle. Feather molt is a timely undertaking that is considered among the three most energy-demanding processes in the life cycle of birds. To deal with time pressure, passerines may shorten their molt duration, using three non–mutually exclusive mechanisms: (1) replacing only part of the plumage, (2) increasing the speed of molt, and (3) postponing the renewal of some or all the plumage to a later season (i.e., from the summer to the overwintering period). We used a comparative approach by measuring 12,349 individuals from 134 passerine species to explore how feather molt of juvenile and adult passerines is evolutionarily modulated under time constraints. The results indicate that breeding at northern latitudes and long-distance migration limit the time available for molt and that the consequences of time constraints were age dependent. While the duration of adult summer molt decreased, the extent, rather than the duration, of juvenile molt declined under time constraints. This study highlights the importance of considering time constraints in order to enhance the understanding of selective forces that shape life-history processes and their consequences throughout the annual routine.
Data from: The evolution of the dicynodont sacrum: constraint and innovation in the synapsid axial column
Constraint is a universal feature of morphological evolution. The vertebral column of synapsids (mammals and their close relatives) is a classic example of this phenotypic restriction, with greatly reduced variation in the number of vertebrae compared to the sauropsid lineage. Synapsids generally possess only three sacral vertebrae, which articulate with the ilium and play a key role in locomotion. Dicynodont anomodonts are the exception to this rule, possessing seven or more sacral vertebrae while reaching a range of body sizes rivaled among synapsids only by therian mammals. Here we explore the evolution of this unusual sacral morphology in dicynodonts by 1) hypothesizing homologies of the additional sacral vertebrae, 2) using ancestral state reconstruction and phylogenetic regressions (e.g., logistic regression, Poisson regression) to track the coevolution of sacral count and body size, and 3) proposing mechanisms by which additional sacral vertebrae were incorporated during dicynodont evolution. We find that sacral vertebra morphology covaries with sacral count in consistent ways across dicynodonts, implying that sacra with a given number of vertebrae are composed of homologous elements. There is a correlation between increased sacral count and larger body size, especially at the shift from four to five sacrals near the origin of Bidentalia. Based on position, morphology, and the consistent number of presacral vertebrae among dicynodonts, we hypothesize that the additional sacrals anterior to the plesiomorphic three are duplications of the first sacral, and that a single caudosacral was incorporated by a shift in the identity of the anteriormost caudal vertebra. Although changes in sacral count appear to be correlated with shifts in body size in dicynodonts, the evolution of general morphological conservativism in the synapsid sacrum remains to be further explored.
Data - Prescribed fire in longleaf pine ecosystems: fire managers' perspectives on priorities, constraints, and future prospects
<p><strong>The following information describes the data coded in the corresponding database as it relates to survey question responses. This dataset includes all data used to produce graphs in:</strong></p><p>Kupfer, J.A., Lackstrom, K., Grego, J.M., Dow, K., Terando, A.J., and Hiers, J.K. 2022. Perspectives on prescribed fire management in longleaf pine ecosystems: Current constraints and future prospects. <i>Fire Ecology</i> 18, 27. <a href="https://doi.org/10.1186/s42408-022-00151-6">https://doi.org/10.1186/s42408-022-00151-6</a>..</p><p><strong>Part I: Criteria for Prioritizing Burn Sites (Columns A-S)</strong></p><p><strong>Question #1: "For the characteristics listed below, please rank your top 3 criteria for determining whether a site has a high priority for burning." Scale: 1 = highest, 2 = next; 3 = next; NULL = not in top 3. Columns K-S indicate simply whether a criterion was chosen (1) or not (0), regardless of rank.</strong></p><p>A. Participant ID</p><p>B. TimeSinceBurnRk = Time since the last fire </p><p>C. FuelReductionRk = Burn to reduce fuels</p><p>D. EcoHealthRk = Burn to improve ecosystem health</p><p>E. TimberRk = Improving timber</p><p>F. FirebreaksRk = Presence of firebreaks</p><p>G. TandERk = Burn to assist T&E species</p><p>H. WUIrk = Proximity to Wildland Urban Interface</p><p>I. ExoticInvasiveRk = Prioritizing due exotics / invasives</p><p>J. OtherCritRk = Other criteria not included above</p><p>K. TimeSinceBurn = Time since the last fire (1 = cited in any order; NULL = not in top 3)</p><p>L. FuelReduction = Burn to reduce fuels (same ranking as previous)</p><p>M. EcoHealth = Burn to improve ecosystem health (same ranking as previous)</p><p>N. Timber = Improving timber (same ranking as previous)</p><p>O. Firebreaks = Presence of firebreaks (same ranking as previous)</p><p>P. TandE = Burn to assist T&E species (same ranking as previous)</p><p>Q. WUI = Proximity to Wildland Urban Interface (same ranking as previous)</p><p>R. ExoticInvasive = Prioritizing due exotics / invasives (same ranking as previous)</p><p>S. OtherCrit = Other criteria not included above (same ranking as previous)</p><p> </p><p><strong>Part II: Longleaf Pine Burning Frequency (T-U)</strong></p><p><strong>Question #3: "Recommendations for the frequency of prescribed burns depend on the local conditions, including: 1) the diversity of sites with longleaf ecosystems, 2) variation in the suite of understory species that define the habitat, and 3) the 'historic' fire return interval. How often, on average, should longleaf pine stands in your unit(s) be burned?</strong></p><p>T. RecentBurnFreq: 1 = < every 2 yrs, 2 = every 2-4 yrs, 3 = every 4-5 yrs, 4 = > every 5 yrs </p><p><strong>Question #4. In practice, how often, on average, have longleaf pine units in your area been burned over the past 10 years?</strong></p><p>U. CompBurnFreq: -1 = less frequently than previous answer; 0 = as frequently as previous answer; 1 = more frequently than previous answer (the response was compared to that from Question 3 and scored comparatively).</p><p> </p><p><strong>Part III: Current legal, institutional, and managerial constraints (V-AB)</strong></p><p><strong>Question #6. In this section we would appreciate your help in better understanding how specific factors constrain or limit your ability to conduct prescribed burns. Do the following factors pose constraints to prescribed burning at your units? Scale: 1 = not a constraint; 2 = sometimes a constraint; 3 = commonly a constraint; -9999 = Not applicable. (note: #NULL! = no response)</strong></p><p>V. ConstrPublic: Burning constrained by public concerns </p><p>W. ConstrWUI: Burning constrained by nearby development </p><p>X. ConstrRisk: Burning constrained by concerns about liability, career, etc.</p><p>Y. ConstrPartners: Burning constrained by partnerships and agreements</p><p>Z. ConstrIncent: Limited incentives or organizational pushback</p><p>AA. ConstrLegal: Legal constraints (e.g. NEPA, etc.)</p><p>AB. ConstrOther: Other constraints</p><p><strong> </strong></p><p><strong>Part IV: Environmental and resource constraints (AC-AJ)</strong></p><p><strong>Question 7: Please indicate how often these factors constrain prescribed burning in your management unit during the longleaf pine dormant season and growing season. Scale: 1 = not a constraint; 2 = sometimes a constraint; 3 = commonly a constraint; -9999 = Not applicable. (</strong><i><strong>note: #NULL! = no response</strong></i><strong>)</strong></p><p>AC. ConstrDormWx: Inappropriate weather conditions during dormant season</p><p>AD. ConstrDormFuel: Concerns about fuel loads during dormant season</p><p>AE. ConstrDormAQ: Air quality / smoke management issues during dormant season</p><p>AF. ConstrDormRes: Shortage of resources (personnel, money, equip) during dormant season</p><p>AG. ConstrGrowWx: Inappropriate weather conditions during growing season</p><p>AH. ConstrGrowFuel: Concerns about fuel loads during growing season</p><p>AI. ConstrGrowAQ: Air quality / smoke management issues during growing season</p><p>AJ. ConstrGrowRes: Shortage of resources (personnel, money, equip) during growing season</p><p> </p><p><strong>Part V: Seasonal Weather Constraints (AK-BD)</strong></p><p><strong>Question 8: For weather conditions during the dormant season and growing season, please check the boxes for all factors which are common constraints on prescribed burning. (scale: 1 = factor selected, #NULL! = factor not selected).</strong></p><p>AK. ConstrHiTDorm: High temperature is a common constraint during the dormant season</p><p>AL. ConstrHiTGrow: High temperature is a common constraint during the growing season</p><p>AM. ConstrLowTDorm: Low temperature is a common constraint during the dormant season</p><p>AN. ConstrLowTGrow: Low temperature is a common constraint during the growing season</p><p>AO. ConstrHiRHDorm: High relative humidity is a common constraint during the dormant season</p><p>AP. ConstrHiRHGrow: High relative humidity is a common constraint during the growing season</p><p>AQ. ConstrLowRHDorm: Low relative humidity is a common constraint during the dormant season</p><p>AR. ConstrLowRHGrow: Low relative humidity is a common constraint during the growing season</p><p>AS. ConstrHiWindDorm: High winds are a common constraint during the dormant season</p><p>AT. ConstrHiWindGrow: High winds are a common constraint during the growing season</p><p>AU. ConstrLowWindDorm: Low winds are a common constraint during the dormant season</p><p>AV. ConstrLowWindGrow: Low winds are a common constraint during the growing season</p><p>AW. ConstrDaysRainDorm: # of days since last rain is a common constraint during the dormant season</p><p>AX. ConstrDaysRainGrow: # of days since last rain is a common constraint during the growing season</p><p>AY. ConstrDroughtDorm: Drought conditions are a common constraint during the dormant season</p><p>AZ. ConstrDroughtGrow: Drought conditions are a common constraint during the growing season</p><p>BA. ConstrAtmoDispDorm: Low atmospheric dispersion is a common constraint during the dormant season</p><p>BB. ConstrAtmoDispGrow: Low atmospheric dispersion is a common constraint during the growing season</p><p>BC. ConstrTransWindDorm: Adequate transport winds are a common constraint during the dormant season</p><p>BD. ConstrTransWindGrow: Adequate transport winds are a common constraint during the growing season</p><p><strong> </strong></p><p><strong>Part VI: Expectations for future changes in burning constraints due to climate change and urban growth (BE-BW)</strong></p><p><strong>Question 9: To what extent do you think climate change currently affects your management decisions? </strong></p><p>BE. CurrClimChange. Scale: 0 = not at all; 1 = a little; 2 = moderately; 4 = a great deal; -9999 = don't know. #NULL! = no response.</p><p><strong>Question 10: To what extent do you think urban growth currently affects your management decisions? </strong></p><p>BF. CurrUrbanGrowth: Scale: 0 = not at all; 1 = a little; 2 = moderately; 4 = a great deal; -9999 = don't know. #NULL! = no response.</p><p><strong>Question 12:</strong> <strong>To what extent do you think future climate change (e.g., increasing temperature, more intense rainstorms, and/or extreme weather events) will affect your prescribed burning decisions? </strong>. <strong>Scale for all variables: 0 = not at all; 1 = a little; 2 = moderately; 3 =a great deal; -9999 = don't know. #NULL! = no response.</strong></p><p>BG. ClimChangeShort: Effects over the next 5-10 years</p><p>BH. ClimChangeMed: Effects over the next 10-30 years</p><p>BI. ClimChangeLong: Effects over the next 30-50 years</p><p><strong>Question 13:</strong> <strong>To what extent do you think future urbanization patterns (e.g., changes in the wildland urban interface, loss of habitat to restore longleaf pine ecosystems) will affect your prescribed burning decisions? Scale for all variables: 0 = not at all; 1 = a little; 2 = moderately; 3 =a great deal; -9999 = don't know. #NULL! = no response.</strong></p><p>BJ. UrbChangeShort: Effects over the next 5-10 years</p><p>BK. UrbChangeMed: Effects over the next 10-30 years</p><p>BL. UrbChangeLong: Effects over the next 30-50 years</p><p><strong>Question 15: Thirty years from now, which of these constraints do you think will be the most significant constraints to your use of prescribed burning? Please select up to 5 constraints. Scale: 1 = selected; #NULL! = selected</strong></p><p>BM. FutConstrWx: Inappropriate weather conditions (1 = cited in any order; NULL = not in top 5)</p><p>BN. FutConstrFuel: High fuel loads (same scale as previous)</p><p>BO. FutConstrAQ: Air quality issues, including smoke management (same scale as previous)</p><p>BP FutConstrRes: Shortage of resources (personnel, money, equipment) (same scale as previous)</p><p>BQ. FutConstrPublic: Avoiding public objections or concerns over burning (same scale as previous)</p><p>BR. FutConstrWUI: Residential or other development in or near burn areas (same scale as previous)</p><p>BS. FutConstrRisk: Risk aversion (liability, career, political repurcussions) (same scale as previous)</p><p>BT. FutConstrAgree: Challenges posed by agreement and partnerships (same scale as previous)</p><p>BU FutConstrIncent: Limited incentives, institutional history (same scale as previous)</p><p>BV. FutConstrLegal: Legal constraints (same scale as previous)</p><p>BW. FutConstrOther: Other constraints</p><p> </p><p><strong>Part VII: Responder Demographics (BX)</strong></p><p>Respondent provided state and management unit information. Scale 1 = yes; 0 = no.</p>
Emergent constraint regarding carbon-climate feedbacks
<p>Codes for the main analyses</p>
Concrete Constraint Guided Symbolic Execution
<p>Decompress the "reproduction.zip" and refer to README.md to reproduce our work.</p>
Supplementary data to BANMF-S: a blockwise accelerated non-negative matrix factorization framework with structural network constraints for single cell RNA-seq data imputation
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Supercritical fluids cause the Mg-Fe isotope anomaly of arc magma: constraints from an ultrahigh-pressure eclogite-vein system in the Dabie orogen
<p>TableS1-S3</p>
Supplementary Data Tables for Stolper et al. (2022) Constraints on Early Paleozoic deep-ocean oxygen concentrations from the iron geochemistry of the Bay of Islands ophiolite in G-Cubed
<p>Supplementary data tables for Stolper et al. (2022) associated with the paper entitled "Constraints on Early Paleozoic deep-ocean oxygen concentrations from the iron geochemistry of the Bay of Islands ophiolite" published in <em>G-Cubed</em>.</p>
An empirical study of data constraint implementations in Java
<p>Replication package for eponymous EMSE article.</p>
Data from: Rational constraints and the evolution of fairness in the Ultimatum Game
Behavior in the Ultimatum Game has been well-studied experimentally, and provides a marked contrast between the theoretical model of a self-interested economic agent and that of an actual human concerned with social norms such as fairness. How did such norms evolve, when punishing unfair behavior can be costly to the punishing agent? The work described here simulated a series of Ultimatum Games, in which populations of agents earned resources based on their preferences for proposing and accepting (or rejecting) offers of various sizes. Two different systems governing the acceptance or rejection of offers were implemented. Under one system, the probability that an agent accepted an offer of a given size was independent of the probabilities of accepting the other possible offers. Under the other system, a simple, ordinal constraint was placed on the acceptance probabilities such that a given offer was at least as likely to be accepted as a smaller offer. For simulations under either system, agents' preferences and their corresponding behavior evolved over multiple generations. Populations without the ordinal constraint came to emulate maximizing economic agents, while populations with the constraint came to resemble the behavior of human players.
Staying close to home: Ecological constraints on space use and range fidelity of a mountain ungulate
<p>Understanding patterns of animal space use and range fidelity has important implications for species and habitat conservation. For species that live in highly seasonal environments, such as mountain goats (Oreamnos americanus), spatial use patterns are expected to vary in relation to seasonal changes in environmental conditions and sex‐ or age-specific selection pressures. To address hypotheses about sex, age, and seasonality influence on space use ecology, we collected GPS location data from 263 radio‐collared mountain goats (males, n = 140; females, n =123) in coastal Alaska during 2005 – 2016. Location data were analyzed to derive seasonal and sex‐specific fixed-kernel home range estimates, and to quantify the degree of seasonal range and utilization distribution overlap. Overall, we determined that home range size was smallest during winter, expanded coincident with the onset of green-up and parturition, and were largest during summer. Home range size of males and females did not differ significantly during winter, but females had larger home ranges than males during summer; a relationship that was switched during the mating season. Pairwise comparisons involving individual females across subsequent years indicated home ranges were significantly smaller during years when they gave birth to offspring. Mountain goats exhibited a strong degree of range fidelity, and 99% (n =138) of individual animals returned to their previous year's seasonal range with an average annual Bhattacharyya's Affinity utilization distribution overlap index of 68%. Similarity of seasonal home range utilization distributions varied in relation to sex and season in some respects. Home range overlap was highest during the summer vegetation growing season, particularly among females. These findings advance our understanding about how environmental variation and sex- and age-related reproductive constraints influence space use and range fidelity among alpine ungulates. Documentation of the high degree of range fidelity among mountain goats has important conservation implications in landscapes increasingly altered by anthropogenic activities.</p>
Isotope constraints on nitrogen dynamics in the upper water column of the South China Sea
<p>ds01-All water sample data supporting this study are summed in ds01.</p> <p>ds02-All CTD data supporting this study are summed in ds02.</p>
Supplementary material 1 from: Connolly BM, Powers J, Mack RN (2017) Biotic constraints on the establishment and performance of native, naturalized, and invasive plants in Pacific Northwest (USA) steppe and forest. NeoBiota 34: 21-40. https://doi.org/10.3897/neobiota.34.10820
Table S1 : Explanation note: Site sites with UTM coordinates and elevation a.s.l.
The Origin of Magnetofossil Coercivity Components: Constraints from Coupled Experimental Observations and Micromagnetic Calculations
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
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